Introduction

Drought, unlike other natural disasters, develops gradually, making its onset hard to detect. Effective water management requires predictive models to monitor drought characteristics. Aspas Plain, one of the fertile plains in Fars province, faces drought threats. This study evaluates drought conditions using indices such as the Standard Precipitation Index (SPI) and Drought Intensity Index (DI). This research utilizes Dempster-Shafer evidence theory to measure and quantify the uncertainties associated with these indicators in the drought assessment process. Such analysis aids in better drought planning and response strategies.

Methods
The research employed 36 years of climate data (1986–2021) collected from meteorological and hydrological stations in the region. Five key drought indices—SPI (Standardized Precipitation Index), DI (Decile Index), PN (Percent of Normal Index), RDI (Reconnaissance Drought Index), and SDI (Standardized Discharge Index)—were used to monitor drought conditions at monthly, seasonal, and annual scales. The Dempster-Shafer theory, which allows the integration of multiple evidence sources, was applied to quantify uncertainties and provide a probabilistic framework for assessing drought conditions.

Results
Occurrence of Drought Events:
Analysis of drought indices revealed consistent evidence of severe droughts in Aspas Plain during several periods, particularly between 2012 and 2017. However, due to differences in the calculation methods and parameters of the indices, the results varied for other time periods, underscoring the presence of uncertainties in drought assessments.

Uncertainty Analysis:
The uncertainty analysis indicated that the level of uncertainty in estimating normal climatic conditions ranged between 72% and 79%, for drought conditions between 35% and 44%, and for wet conditions between 31% and 47%. These results highlight the challenges posed by using multiple indices with diverse methodologies to estimate climatic conditions.

Discussion
The findings underscore the complexity of monitoring drought using various indices, as each index provides unique insights based on its specific parameters. The Dempster-Shafer theory proved to be a robust tool for synthesizing these insights and quantifying uncertainty. Differences in index results also point to the need for more comprehensive modeling approaches to improve the reliability of drought forecasting.

Conclusions
This research demonstrated that combining multiple drought indices using the Dempster-Shafer theory enhances the ability to assess drought conditions comprehensively. This integrative approach not only offers a more reliable understanding of drought risk but also supports better planning and management of water resources. Furthermore, the high levels of uncertainty observed in the study highlight the necessity of improving data quality and leveraging advanced modeling techniques for more accurate drought monitoring.

Methods
The research employed 36 years of climate data (1986–2021) collected from meteorological and hydrological stations in the region. Five key drought indices—SPI (Standardized Precipitation Index), DI (Decile Index), PN (Percent of Normal Index), RDI (Reconnaissance Drought Index), and SDI (Standardized Discharge Index)—were used to monitor drought conditions at monthly, seasonal, and annual scales. The Dempster-Shafer theory, which allows the integration of multiple evidence sources, was applied to quantify uncertainties and provide a probabilistic framework for assessing drought conditions.

Results
Occurrence of Drought Events:
Analysis of drought indices revealed consistent evidence of severe droughts in Aspas Plain during several periods, particularly between 2012 and 2017. However, due to differences in the calculation methods and parameters of the indices, the results varied for other time periods, underscoring the presence of uncertainties in drought assessments.

Uncertainty Analysis:
The uncertainty analysis indicated that the level of uncertainty in estimating normal climatic conditions ranged between 72% and 79%, for drought conditions between 35% and 44%, and for wet conditions between 31% and 47%. These results highlight the challenges posed by using multiple indices with diverse methodologies to estimate climatic conditions.

Discussion
The findings underscore the complexity of monitoring drought using various indices, as each index provides unique insights based on its specific parameters. The Dempster-Shafer theory proved to be a robust tool for synthesizing these insights and quantifying uncertainty. Differences in index results also point to the need for more comprehensive modeling approaches to improve the reliability of drought forecasting.

Conclusions
This research demonstrated that combining multiple drought indices using the Dempster-Shafer theory enhances the ability to assess drought conditions comprehensively. This integrative approach not only offers a more reliable understanding of drought risk but also supports better planning and management of water resources. Furthermore, the high levels of uncertainty observed in the study highlight the necessity of improving data quality and leveraging advanced modeling techniques for more accurate drought monitoring.

Methods
The research employed 36 years of climate data (1986–2021) collected from meteorological and hydrological stations in the region. Five key drought indices—SPI (Standardized Precipitation Index), DI (Decile Index), PN (Percent of Normal Index), RDI (Reconnaissance Drought Index), and SDI (Standardized Discharge Index)—were used to monitor drought conditions at monthly, seasonal, and annual scales. The Dempster-Shafer theory, which allows the integration of multiple evidence sources, was applied to quantify uncertainties and provide a probabilistic framework for assessing drought conditions.

Results
Occurrence of Drought Events:
Analysis of drought indices revealed consistent evidence of severe droughts in Aspas Plain during several periods, particularly between 2012 and 2017. However, due to differences in the calculation methods and parameters of the indices, the results varied for other time periods, underscoring the presence of uncertainties in drought assessments.

Uncertainty Analysis:
The uncertainty analysis indicated that the level of uncertainty in estimating normal climatic conditions ranged between 72% and 79%, for drought conditions between 35% and 44%, and for wet conditions between 31% and 47%. These results highlight the challenges posed by using multiple indices with diverse methodologies to estimate climatic conditions.

Discussion
The findings underscore the complexity of monitoring drought using various indices, as each index provides unique insights based on its specific parameters. The Dempster-Shafer theory proved to be a robust tool for synthesizing these insights and quantifying uncertainty. Differences in index results also point to the need for more comprehensive modeling approaches to improve the reliability of drought forecasting.

Conclusions
This research demonstrated that combining multiple drought indices using the Dempster-Shafer theory enhances the ability to assess drought conditions comprehensively. This integrative approach not only offers a more reliable understanding of drought risk but also supports better planning and management of water resources. Furthermore, the high levels of uncertainty observed in the study highlight the necessity of improving data quality and leveraging advanced modeling techniques for more accurate drought monitoring.